Please use Python (sklearn etc) for this. Thanks!

Dataset to be loaded: https://github.com/summerwaters11/polydata

Problem 3. Polynomial Regression. Load the dataset poly_data.csv. The rst column is a vector of predictors :r and the second column is a vector of responses 3;. Suppose we believe it was generated by some polynomial of the predictors with Gaussian error, and we would like to recover the true coefcients of the underlying process. A polynomial regression can be estimated by including all powers of X as predictors in the model. For example, to estimate a quadratic regression, we include the predictors 1c and 3:2 as well as t the intercept. (a) Pick a set of polynomial models. Compute the kfold cross validation error with respect to mean squared error for each of these models. Report the value of k that you use and plot the crossvalidation error as a function of polynomial power. (b) Choose a model from your initial set, and rerun on the entire data set. Report the coefcients and make a scatter plot of a: and y with your tted polynomial. Justify your selection